US10037583B2 - Systems and methods for analyzing social network content of a key influencer - Google Patents

Systems and methods for analyzing social network content of a key influencer Download PDF

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US10037583B2
US10037583B2 US14/493,664 US201414493664A US10037583B2 US 10037583 B2 US10037583 B2 US 10037583B2 US 201414493664 A US201414493664 A US 201414493664A US 10037583 B2 US10037583 B2 US 10037583B2
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influencer
influencers
cloud
key influencers
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Shaurabh Bharti
Jai Ganesh
Nishtha Srivastava
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Infosys Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/01—Social networking

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  • the invention relates generally to analyze social network content, and in particular, to a system and method for analyzing social network content of a key influencer.
  • the World Wide Web is a vast repository of information that connects people, providing them access to millions of web resources via the Internet.
  • Social Networks are growing exponentially, which presents challenges for enterprises who want to monitor and mine these social networks.
  • Social Network Analysis relates to mapping, understanding, and analyzing interactions across a set of people.
  • Social networks both formal as well as informal can foster knowledge sharing among participants.
  • the exchanges that take place in social networking environments go beyond providing direct value to the user. It fosters collaboration among participants and can lead to aggregation of highly influential content and ideas within various types of social media.
  • Content generated in social networking environments would include discussion threads, logs of chat room conversations, contents of blogs, and any other content posted by users.
  • This collection of content comes from original sources (creation of the user), referenced sources (material cited and presented by users) and aggregated content (collection of material assembled in a unique manner). As long as members continue to add useful or relevant content to the group, the positive network externalities would draw new members to the group.
  • the accumulated content and ideas within successful social networking environments thus becomes an aggregation of the collective intelligence of the user community participating in those sites.
  • the accumulated content can be considered as an asset that has value, which can be tapped through the right types of analyses.
  • This asset has potential value to both owners of the sites as well as the organizations whose products and services being discussed. It presents significant implications for enterprises wanting to leverage social networks to draw insights and inferences on user participation and preferences expressed in networks. Thus, to monitor and analyze the content posted in a social network by a key influencer becomes very important to enhance decision making ability of any organization.
  • a method for analyzing content associated with one or more influencers of at least one social network is disclosed.
  • the method includes identifying one or more key influencers with respect to a topic of interest in at least one social network. Thereafter, an influencer topic cloud for each of the one or more key influencers and an overall topic cloud for the topic of interest are created. The identification of the one or more key influencers is cross-verified by comparing one or more attributes of the influencer topic cloud with the overall topic cloud. Further, volume of social interaction of the one or more key influencers with respect to the topic of interest is determined, wherein the volume of the social interaction comprises interaction with peers and the interaction with followers. Finally, the volume of the social interaction of the one or more key influencers is visualized.
  • a system for analyzing content associated with one or more influencers of at least one social network includes a key influencer identifier, a topic cloud creator, a cross-verifier, a social interaction determiner and a visualizer.
  • the key influencer identifier is configured for identifying one or more key influencers with respect to a topic of interest in at least one social network.
  • the topic cloud creator is configured for creating an influencer topic cloud for each of the one or more key influencers and an overall topic cloud for the topic of interest.
  • the cross-verifier is configured for cross-verifying the identification of the one or more key influencers by comparing one or more attributes of the influencer topic cloud with the overall topic cloud.
  • the social interaction determiner is configured for determining volume of social interaction of the one or more key influencers with respect to the topic of interest, wherein the volume of the social interaction comprises interaction with peers and the interaction with followers.
  • the visualizer is configured for visualizing the volume of the social interaction of the one or more key influencers on a display of a computing device.
  • a computer readable storage medium for analyzing content associated with one or more influencers of at least one social network.
  • the computer readable storage medium which is not a signal stores computer executable instructions for identifying one or more key influencers with respect to a topic of interest in at least one social network, creating an influencer topic cloud for each of the one or more key influencers and an overall topic cloud for the topic of interest, cross-verifying the identification of the one or more key influencers by comparing one or more attributes of the influencer topic cloud with the overall topic cloud, determining volume of social interaction of the one or more key influencers with respect to the topic of interest, wherein the volume of the social interaction comprises interaction with peers and the interaction with followers and visualizing the volume of the social interaction of the one or more key influencers.
  • FIG. 1 is a computer architecture diagram illustrating a computing system capable of implementing the embodiments presented herein.
  • FIG. 2 is a flowchart, illustrating a method for analyzing content associated with one or more influencers of at least one social network, in accordance with an embodiment of the present invention.
  • FIG. 3 is an exemplary visualization of volume of social interaction of one or more key influencers, in accordance with an embodiment of the present invention.
  • FIG. 4 is a block diagram illustrating a system for analyzing content associated with one or more influencers of at least one social network, in accordance with an embodiment of the present invention.
  • Exemplary embodiments of the present invention provide a system and method for analyzing content associated with one or more influencers of at least one social network. This involves identifying key influencers of at least one social network with respect to a topic of interest. Thereafter, an overall topic cloud and an influencer topic cloud for each key influencer is created and analyzed. The overall topic cloud and an influencer topic cloud are compared to cross-verify if the identification of the key influencers is correct. After that, volume of social interaction of the key influencers are determined and visualized.
  • FIG. 1 illustrates an example of a suitable computing environment 100 in which all embodiments, techniques, and technologies of this invention may be implemented.
  • the computing environment 100 is not intended to suggest any limitation as to scope of use or functionality of the technology, as the technology may be implemented in diverse general-purpose or special-purpose computing environments.
  • the disclosed technology may be implemented using a influencer analyzing computing device (e.g., a server, desktop, laptop, hand-held device, mobile device, PDA, etc.) comprising a processing unit, memory, and storage storing computer-executable instructions implementing the service level management technologies described herein.
  • the disclosed technology may also be implemented with other computer system configurations, including hand held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, a collection of client/server systems, and the like.
  • the computing environment 100 includes at least one central processing unit 102 and memory 104 .
  • the central processing unit 102 executes computer-executable instructions. In a multi-processing system, multiple processing units execute computer-executable instructions to increase processing power and as such, multiple processors can be running simultaneously.
  • the memory 104 may be volatile memory (e.g., registers, cache, RAM), non-volatile memory (e.g., ROM, EEPROM, flash memory, etc.), or some combination of the two.
  • the memory 104 stores software 116 that can implement the technologies described herein.
  • a computing environment may have additional features.
  • the computing environment 100 includes storage 108 , one or more input devices 110 , one or more output devices 112 , and one or more communication connections 114 .
  • An interconnection mechanism such as a bus, a controller, or a network, interconnects the components of the computing environment 100 .
  • operating system software provides an operating environment for other software executing in the computing environment 100 , and coordinates activities of the components of the computing environment 100 .
  • FIG. 2 is a flowchart, illustrating a method for analyzing content associated with one or more influencers of at least one social network, in accordance with an embodiment of the present invention.
  • One or more key influencers of at least one social network are identified with respect to a topic of interest, as in step 202 .
  • the one or more key influencers can be identified based on the method described in U.S. patent application Ser. No. 13/716,045 or any other method known by the skilled person in this art.
  • an overall topic cloud and an influencer topic cloud for each of the one or more key influencers are created, as in step 204 .
  • An influencer is generally active over a variety of topics.
  • all the topics or keywords related to the topic of interest together form an overall topic cloud.
  • the overall topic cloud and an influencer topic cloud are compared with respect to one or more attributes to cross-verify if the identification of the key influencers is correct, as in step 206 .
  • An overall topic cloud contains posts from all users of the social network with higher or lesser significance. Usually, this is the targeted mass of influence. Lesser significant influencers are abundant but hold a passive presence in an overall topic cloud, creating a mass sentiment around the topic of interest.
  • the influencer topic cloud is compared with the overall topic cloud to match the abundance with significance.
  • the influencer topic cloud having more common attributes to the overall topic cloud represents higher strength of lightening.
  • a volume of social interaction of the one or more key influencers is determined with respect to the topic of interest, as in step 208 .
  • the volume of social interaction of the one or more key influencers includes interaction with peers and interaction with followers.
  • FIG. 3 is an exemplary visualization of volume of social interaction of one or more key influencers, in accordance with an embodiment of the present invention.
  • Central node in the level 0 302 represents the influencer. All other nodes represent users/peers in the network involved in the interaction. Peers having excessive interaction have shorter but thicker edges. Peers having stronger interaction circles of their own have longer edges.
  • Each depth level ( 304 and 306 ) in the well represents interaction circles. Volume of the interaction with followers and peers gives an insight into overall effect of influencer's tweet in a social network.
  • the network data may include, but is not limited to, connection between friends and followers, lists, verification and membership of the one or more key influencers.
  • Friends influence the user with their views, while user influences its followers with its views. Both are important for a constructive communication in social network. They also help in further propagation of the viewpoint to a larger network.
  • User is tagged by other users in lists according to their preferences. They tag the user with list names in the process. These tags provide insight into various ways the user influence upon other users. A verified member as well as an old time member influence stronger on followers.
  • Personal data may include but is not limited to gender, age, race, geography, language, profession and personal interest of the one or more key influencers.
  • Content data may include but is not limited to attributes of Posts (text, video, and photo) like time, size, search relevance, Shares, Comments/responses and so on.
  • the influencer topic cloud of the one or more key influencers are compared with each other for clustering the key influencers.
  • Influencers are arranged based on the strength of lightening between two influencers. This strength is determined based on similarity between attributes of two influencer topic clouds.
  • the one or more attributes may include but are not limited to network data, profile data and content data of the one or more influencers, size of a keyword based on its frequency, classification based on one or more related or unrelated topics in question, a peripheral ring around the keyword capturing share of the one or more influencers, classification based on age of the content or combination thereof.
  • FIG. 4 is a block diagram illustrating a system for analyzing content associated with one or more influencers of at least one social network, in accordance with an embodiment of the present invention.
  • the system includes a key influencer identifier 402 , a topic cloud creator 404 , a cross-verifier 406 , a key influencer cluster generator 408 , a social interaction determiner 410 , a visualizer 412 and a self-refiner 414 .
  • the influencer identifier 402 is configured for identifying one or more key influencers with respect to a topic of interest in at least one social network.
  • the topic cloud creator 404 is configured for creating an influencer topic cloud for each of the one or more key influencers and an overall topic cloud for the topic of interest.
  • the cross-verifier 406 is configured for cross-verifying the identification of the one or more key influencers by comparing one or more attributes of the influencer topic cloud with the overall topic cloud.
  • the key influencer cluster generator 408 is configured for generating cluster of the one or more key influencers by comparing the influencer topic cloud with each other based on one or more attributes. The one or more attributes are mentioned with respect to FIG. 2 .
  • the social interaction determiner 410 is configured for determining volume of social interaction of the one or more key influencers with respect to the topic of interest, wherein the volume of the social interaction comprises interaction with peers and the interaction with followers.
  • the visualizer 412 is configured for visualizing the volume of the social interaction of the one or more key influencers on a display of a computing device.
  • FIG. 3 is an exemplary visualization of volume of social interaction of one or more key influencers, in accordance with an embodiment of the present invention.
  • the visualizer further visualizes network data, profile data and content data of the one or more influencers. The details about network data, profile data and content data are described with reference to FIG. 2 .
  • the self-refiner 414 is configured for refining the system based on preferences and feedback of one or more users. Self-refinement of the system may depend on the following:

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Citations (4)

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US20130179806A1 (en) * 2012-01-05 2013-07-11 International Business Machines Corporation Customizing a tag cloud
US20150120782A1 (en) * 2013-10-25 2015-04-30 Marketwire L.P. Systems and Methods for Identifying Influencers and Their Communities in a Social Data Network
US20160191447A1 (en) * 2014-12-30 2016-06-30 Crimson Hexagon, Inc. Analyzing interests based on social media data

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080104225A1 (en) * 2006-11-01 2008-05-01 Microsoft Corporation Visualization application for mining of social networks
US20130179806A1 (en) * 2012-01-05 2013-07-11 International Business Machines Corporation Customizing a tag cloud
US20150120782A1 (en) * 2013-10-25 2015-04-30 Marketwire L.P. Systems and Methods for Identifying Influencers and Their Communities in a Social Data Network
US20160191447A1 (en) * 2014-12-30 2016-06-30 Crimson Hexagon, Inc. Analyzing interests based on social media data

Non-Patent Citations (2)

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Newman M.E.J.: Analysis of weighted networks. Physical Review E 70M 056131, Nov. 2004. *

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